#001 Import and Plot data for one stock:¶

Define and import data, save as CSV¶

Import libraries

  • Link to Pandas Documentation: https://pandas.pydata.org/docs/index.html

  • Link to datareader Documentation: https://pandas-datareader.readthedocs.io/en/latest/index.html

  • Link to Yf Documentation: https://pandas-datareader.readthedocs.io/en/latest/readers/yahoo.html

  • Link to Plotly Documentation: https://plotly.com/python/

In [1]:
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install yfinance
#    !pip install datetime
#    !pip install plotly_express
In [2]:
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import yfinance as yf
import datetime as dt
import plotly.express as px
import plotly.graph_objects as go
In [3]:
# Define the start and end dates, last 5 years
end = dt.datetime.now()
start = end - dt.timedelta(days = 365*5)
In [4]:
# define Tickers
tk = input('Enter the ticker code: ')
tickers = [tk]
file_name = tk + ".csv"

yf.pdr_override()

Enter the ticker code: META
In [5]:
#obtain data and save as CSV
df = pdr.get_data_yahoo(tickers, start = start, end = end)
df.to_csv(file_name)
df.head(2)

[*********************100%%**********************]  1 of 1 completed
Out[5]:
Open High Low Close Adj Close Volume
Date
2018-11-12 144.479996 145.039993 140.490005 141.550003 141.550003 18542100
2018-11-13 142.000000 144.880005 141.619995 142.160004 142.160004 15141700

Import CSV and test values¶

In [6]:
#read CSV file
stock_df = pd.read_csv(file_name)
In [7]:
# Test for null values, and show info
stock_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1258 entries, 0 to 1257
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       1258 non-null   object 
 1   Open       1258 non-null   float64
 2   High       1258 non-null   float64
 3   Low        1258 non-null   float64
 4   Close      1258 non-null   float64
 5   Adj Close  1258 non-null   float64
 6   Volume     1258 non-null   int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 68.9+ KB

Calculate Daily Returns from one stock, and insert on DataFrame¶

In [8]:
#insert 'Daily Return' column
stock_df['Daily Return'] = stock_df['Adj Close'].pct_change(1) * 100

# replace the first row, changes null for 0
stock_df['Daily Return'].replace(np.nan, 0, inplace = True)
stock_df
Out[8]:
Date Open High Low Close Adj Close Volume Daily Return
0 2018-11-12 144.479996 145.039993 140.490005 141.550003 141.550003 18542100 0.000000
1 2018-11-13 142.000000 144.880005 141.619995 142.160004 142.160004 15141700 0.430944
2 2018-11-14 143.699997 145.580002 141.550003 144.220001 144.220001 22068400 1.449070
3 2018-11-15 142.330002 144.839996 140.830002 143.850006 143.850006 30320300 -0.256549
4 2018-11-16 141.070007 141.770004 137.770004 139.529999 139.529999 37250600 -3.003133
... ... ... ... ... ... ... ... ...
1253 2023-11-06 315.980011 318.329987 314.450012 315.799988 315.799988 12887700 0.381431
1254 2023-11-07 317.059998 321.000000 315.119995 318.820007 318.820007 14055600 0.956308
1255 2023-11-08 318.140015 321.329987 314.880005 319.779999 319.779999 13609700 0.301108
1256 2023-11-09 319.420013 324.179993 318.799988 320.549988 320.549988 16103100 0.240787
1257 2023-11-10 319.940002 329.100006 319.459991 328.769989 328.769989 19096200 2.564343

1258 rows × 8 columns

In [9]:
stock_df.describe().round(2)
Out[9]:
Open High Low Close Adj Close Volume Daily Return
count 1258.00 1258.00 1258.00 1258.00 1258.00 1.258000e+03 1258.00
mean 230.41 233.76 227.30 230.55 230.55 2.376742e+07 0.11
std 69.13 69.66 68.45 69.07 69.07 1.560471e+07 2.78
min 90.08 90.46 88.09 88.91 88.91 6.046300e+06 -26.39
25% 178.24 180.40 176.29 178.09 178.09 1.505675e+07 -1.16
50% 213.19 216.04 210.51 213.33 213.33 2.012345e+07 0.11
75% 288.38 292.74 284.33 288.26 288.26 2.797518e+07 1.43
max 381.68 384.33 378.81 382.18 382.18 2.323166e+08 23.28

Ploting Results¶

Used Plotly Express for data visualization

In [10]:
# Define a function that performs interactive data visualization using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    # starts with 1, to skip the date column
    
    for i in df.columns[1:]:
        # Add range slider
        fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
    fig.show()
In [11]:
# Ploting AdjClose
plotly_data(stock_df.iloc[:, [0 , 5]], (tk + ' - Ajusted Closing Price[$]'))

# Ploting Trade Volume
plotly_data(stock_df.iloc[:, [0 , 6]], (tk +' - Trading Volume'))

# Ploting Percentage Daily Return
plotly_data(stock_df.iloc[:, [0 , 7]], (tk +' - Percentage Daily Return [%]'))
In [ ]: